Make AI Work For You

Request a personalized demo to see how Eightfold supports hiring, retention, mobility, and diversity initiatives.

Thank you! The information has been submitted successfully.

[contact-form-7 id="494" title="Subscribe Form"]

Author: Ashutosh Garg

With 6000+ research citations, 50+ patents, 35+ peer reviewed research publications, and the outstanding PhD thesis award from UIUC for his PhD thesis in Machine Learning, it’s fair to say that Ashu is one of the world’s experts in machine-learning. After his time managing Search and Personalization efforts at both Google and IBM Research, Ashu founded Bloomreach, a Visionary vendor on Gartner’s magic quadrant for Digital Experience Platforms. Now, he is applying his experience to the problem he is most truly passionate about – helping the world’s talent find their most meaningful and fulfilling work.

An interview with Ashutosh Garg, CEO of Eightfold.ai.

Every week I interview entrepreneurs and experts from around the world to share their big idea about new forms of value creation and the potential we can unlock when technology augments the unique strengths of people to deliver remarkable impact.

I got inspired by the big idea behind Eightfold AI; hence I invited CEO Ashutosh Garg to my podcast. We explore the challenges around the recruitment process as we know it today and discuss how product innovation can help to change the paradigm.

The thing that triggered me most from my interview with Ashutosh

“Instead of people applying for a job, we recommend them.”

Why did this trigger me? What’s the bigger value here?

The value behind this statement could change the way we look at hiring people forever. Recruitment has always been driven by procedures defined decades ago: We have a job opening, we create a job profile, send it out, and wait for responses. Although this seems the most obvious approach, it has many flaws.

Here are just two examples of how this old system is flawed:

Bias: We decide based on who we think is the right candidate – influenced by who we know, and what we think we see.

Timing: We miss out on perfect candidates who become available when we don’t have a job opening.

Based on just these two examples the expectation is that 80% of people today end up applying for the wrong job – and that’s serious!

So, to eliminate this problem, Eightfold decided to turn the problem on its head – using the power of AI to uncover the potential of any individual and make connections intelligently, thereby removing the bias and timing issues. That’s a win-win for everybody.

What’s the most significant opportunity that intelligent matching creates?

It’s the opportunity to solve the war for talent at a global scale. Here’s how.

Let’s take the role of Data Scientist. This is possibly the job in highest demand these days. Typically, companies are looking for the perfect candidate who masters the skill today, ideally with 5 years’ experience. The problem this brings is scarcity, and unfair competition.

So, what if AI could help by approaching the challenge in a different way: Predicting which people could actually be expert data scientists by connecting the dots on what people are capable of doing based on the blend of skills, training and expertise they have gained in other areas? Wouldn’t this make the process much simpler?

(Spoiler alert: It would make the process simpler, and that kind of AI is here now.)

One of the reasons Eightfold.ai is making such significant waves in the talent and HR community is that its founders have a passion for the unique features of the American labor market and what it can offer. Nowhere else is such high quality talent concentrated in one country—which is why it’s no surprise immigrants including Eightfold CEO and founder Ashutosh Garg have come to the US to make the most of their talents.

We’re thrilled that Ashutosh was recently able to relate his life story to Thrive Global, including how his own immigrant experience has meshed with his expertise in Machine Learning to help produce Eightfold’s AI and Machine Learning driven tools to close the talent gap. For Ashutosh and everyone at Eightfold, the US is the arena where the world’s top talent can make the most of its abilities, and Eightfold is working to make that talent as accessible as possible to firms that need it to drive the world’s innovation.

In today’s job market, qualified candidates are a wholly invaluable resource. While the balance of power is always shifting, right now employers generally need qualified candidates more than candidates need job openings. This kind of job market can be thought of as a “candidate’s market” because on the whole, candidates have more leverage than employers when interviewing and negotiating positions. In this kind of job market, the reality is that the burden is on companies to figure out how to effectively attract and retain high-quality employees. In spite of these job market economics, companies across industries continue to rely on decades-old hiring practices, not because they’re necessarily effective, but because they’re so ingrained in the corporate culture that it would take a major leap of thought to go beyond them.

Many firms, particularly in industries including high tech that struggle to attract technically-skilled talent, have fallen back on what are in essence gimmicks to make themselves appealing. Perks like the now almost stereotypical beer taps in the office and foosball table in the basement might be attractive, but they don’t really affect or reflect company culture, attract the right talent, or ensure retention. These tactics may be effective in filling seats in the office, but they don’t logically work to build effective teams. Given the choice between building an ineffective team with beer and foosball, or an effective team without them, which would most companies choose? With new tools built on AI and automation, including those Eightfold will discuss at the virtual summit “Innovating the Candidate Experience.” July 10th through 12th, companies can now differentiate in the ways that really matter.

How to differentiate the right way

Gimmicky hiring tactics are rampant in industries with tough-to fill positions, but they are a desperate move that won’t attract top talent. To attract top talent in a candidate’s job market, companies must figure out how to turn the tables and drive candidate interest in their companies. By building a pool of candidates that are crazy about a company and make that company their first choice regardless of the economic forces of the job market, firms can create a hiring strategy that can weather any change in the market. Even Google, Facebook, and Amazon, who are capable of doing so, will need to develop better ways of managing them.

The key is to give every candidate a great experience, because even if they’re not right for open positions today, they might well be at some point down the road. However, this is often easier said than done. While candidates understand that they hold much of the power in today’s job market, they also understand that their job search will probably involve sending dozens or more copies of their resume to companies and never hear anything back again. If companies can figure out how to engage these valuable and interested applicants, whether they’re right for the company today or not, they’ll find themselves miles ahead of their peers who are adding to the lamentable job market-wide resume black hole.

The specific tasks necessary to carry out this engagement are simple, but tough to pull off. They include:

Keeping track of every candidate a company has contact with

Establishing an ongoing relationship with every candidate, communicating that they are valuable to the company, regardless of whether they’re a current fit or not

Communicating to every candidate that it’s the company’s mission to help them progress in their career, whether they’re hired for a current position or not

Recalling the specifics of interaction with each past candidates and determining, possibly years later, when and how to reconnect

This high level of candidate nurturing is no doubt an ideal. Recruiters in particular are often under enormous pressure to tackle the most pressing personnel issues and doing the kind of work necessary to maintain a quality pool of candidates with positive views of the company can be extraordinarily time consuming. No HR department or recruiting team could be expected to pull this off over the long term without specialized tools. Fortunately, those specialized tools are now available. By leveraging automation and artificial intelligence, companies now have access to the computing power to makes these ideals of candidate nurturing a reality.

Learn what ideals you can realize with AI

On July 10th through 12th, Eightfold will present three sessions at the virtual summit “Innovating the Candidate Experience.” In the sessions, we’ll discuss how new technologies leveraging AI are making what was in the past an idealistic pipe dream a reality. For example, in the ideal world, we’d be able to determine the characteristics that make for an ideal candidate before the interview process even starts. With AI tools, this is possible, ensuring interviews return to their rightful status as a final check of an already-vetted candidate, rather than a costly segment of the screening process itself.

In fact, with automation and AI, ideal candidates can be identified from within the entire candidate pool, including that highly receptive but often overlooked pool of candidates who have applied or interviewed with the company before, but were never placed in a position.

At the summit, we’ll also discuss how AI and automation tools are capable of breaking down some of the most persistent challenges in hiring, particularly unconscious bias on the part of interviewers and the ongoing challenge with building a diverse workforce. Join us via BrightTALK July 10th through 12th to discuss these and more critical challenges in hiring.

Taking a Data-Driven Approach to Eliminating Bad Hiring Decisions

A bad hiring decision can cost two to three times the position’s salary and up to a year of lost productivity according to recent interviews with Chief Human Resource Officers in North American technology companies.

81 percent of HR professionals, recruiters and hiring managers admit to having made mistakes leading to bad hiring decisions according to Robert Half.

49 percent of HR professionals and recruiters believe hiring managers underestimate the complexity of making a good hiring decision and often rush the hiring process to get someone onboard quickly, based on a Robert Half Survey.

The candidate looked perfect in every way. From the résumé that matched what you were looking for, to impeccable interviewing and social skills, all validated by great references. Less than 90 days into the role, they‘re struggling. Long, introspective email chains develop between HR recruiters and the hiring manager, wasting valuable time and further adding to the cost of making a bad hiring decision. Steps are taken to define a performance plan while HR attempts to find a replacement, and the cycle begins again.

The Lack of Quality Data Drives Bad Hiring Decisions

HR recruiters and hiring managers, blinded by the urgency to fill a new position and their own biases, rush decisions on new hires, making mistakes on who gets hired for high-demand positions. In essence, every recruiter and hiring manager has a unique decision-making process or personal algorithm they use for making hiring decisions. Instead of relying on all available data about each candidate and pattern matching their skills to the needs of the open jobs, HR recruiters and hiring managers rely entirely on personal experiences, conscious and unconscious biases, and approaches that worked in the past. Relying on approaches and frameworks that worked in the past intuitively makes sense yet increases the probability of making a bad hiring decision as no new data gets considered. Even in the organizations that excel at recruiting, only one in three hires is a good one.

Let’s look at the example of a company needing to staff their engineering and marketing teams. Requisitions are created and signed off, the jobs are advertised and posted on LinkedIn, and résumés begin pouring in. Internal company referral programs are updated with the new positions, and a few referral résumés come in. Engineering and marketing candidates have carefully crafted their résumés to include every keyword in the job description using SEO techniques in the hopes the Applicant Tracking System (ATS) will add them to the queue. Marketing candidates provide portfolios on their websites showing the projects they’ve completed, and many have videos of keynotes given. Recruiters and hiring managers review the ATS queue, select candidates for screening using their judgment which includes conscious and unconscious biases, and schedule interviews. Skype interviews are scheduled, and the process begins with intensity and urgency, as both recruiter and hiring managers know it’s a cutthroat market for good engineers with JavaScript, R, Python, and machine learning skills. Marketing candidates are most often screened based on who they worked for before, where they attended college, with the previous employer’s reputations being weighted as much or more than accomplishments based on recruiters’ and hiring managers’ biases. The urgency of wanting to find a candidate fast reduces and sometimes eliminates time available to consider additional data that could lead to hiring the best candidate. With no data to provide contextual intelligence on candidates, bad hiring decisions keep happening, and the cycle continues.

Breaking the Cycle of Bad Hiring Decisions With Machine Learning

Talent management and recruiting are in need of a makeover. Consider the fact that as far back as 1482, over 530 years ago, one of the greatest geniuses the world has ever known, Leonardo da Vinci, relied on a handwritten résumé to get new work. His résumé lists his ability to build bridges and support warfare, not reflecting the genius who provided the world with a myriad of scientific discoveries and inventions that modernized the world. Nowhere does his résumé reflect his artistic genius that would lead to the Mona Lisa, Last Supper, Vitruvian Man and many other priceless works of art being produced. Imagine not hiring Leonardo da Vinci because his résumé didn’t reflect the artistic dimensions of his skills, realizing after the fact he could have revolutionized an entire arts business. It’s the same with every company today who relies just on résumés alone. Many are not finding the right candidates because they are relying on an over 500-year-old process that hides a complete, unbiased picture of the candidate. Getting beyond résumés, gaining contextual intelligence of candidates and quickly seeing which skill sets are strengthening over time is essential for reducing the probability of making bad hiring decisions.

The quickest path to reducing and eventually eliminating bad hiring decisions is to use machine learning to seek out candidates who most closely resemble high performer’s profiles. Using comparative analysis that goes far beyond the constraints of résumés, machine learning algorithms can in seconds find a pipeline of potential candidates that are most comparable to the digital personas of the highest achieving profiles for each position. Taking this data-driven approach to hiring also removes the potential for personal biases, both conscious and unconscious, from the decision making process. The more data-driven the hiring process, the greater the diversity every company will be able to achieve. Evaluating candidates based on their ability to excel in the role given their proven skills and strength levels the playing field for everyone to compete for the most desirable jobs any company has.

By integrating publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, and ATS tools, eightfold.aihas created a single Talent Intelligence Platform (TIP)™ shown below. Machine learning algorithms parse all public and enterprise data on candidates, looking for the optimal match of career growth, recent publications, and professional overlap with other colleagues to validate achievements mentioned on their résumés, searching for candidates who are the best fit and have the greatest potential. Hiring decisions become data-driven, alleviating the potential for conscious and unconscious biases to influence which candidates are selected and interviewed. Fine-tuning and personalizing hiring decisions at scale become possible for the first time, and the risks of making bad hiring decisions are reduced.

Conclusion

Bad hiring decisions begin a domino effect in any company, dragging down company-wide performance and morale. Reducing the risks of making bad hiring decisions and follow-on costs needs to start with a unified data strategy that provides actionable insights and contextual intelligence to guide hiring decisions. Résumés are an anachronism. Their limited view of candidates’ abilities and unique skills is illustrated by comparing Leonardo da Vinci’s résumé with the detailed insights possible using the Talent Intelligence Platform™. Reducing employee churn, eliminating the wasted months to recover from a bad hiring decision, and the tendency to keep putting good money after bad in a difficult employee situation costs companies millions of hours a year in lost productivity. Getting new hires onboard fast who will excel and help drive growth matters most. It’s time to move beyond the limitations of an outdated process and embrace a more data-driven, accurate and diversity-enabling approach to making hiring decisions.